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Author: Paul N. Edwards
Publisher: MIT Press
ISBN: 0262518635
Category : Technology & Engineering
Languages : en
Pages : 547
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Book Description
The science behind global warming, and its history: how scientists learned to understand the atmosphere, to measure it, to trace its past, and to model its future. Global warming skeptics often fall back on the argument that the scientific case for global warming is all model predictions, nothing but simulation; they warn us that we need to wait for real data, “sound science.” In A Vast Machine Paul Edwards has news for these skeptics: without models, there are no data. Today, no collection of signals or observations—even from satellites, which can “see” the whole planet with a single instrument—becomes global in time and space without passing through a series of data models. Everything we know about the world's climate we know through models. Edwards offers an engaging and innovative history of how scientists learned to understand the atmosphere—to measure it, trace its past, and model its future.
Author: Paul N. Edwards
Publisher: MIT Press
ISBN: 0262518635
Category : Technology & Engineering
Languages : en
Pages : 547
Get Book
Book Description
The science behind global warming, and its history: how scientists learned to understand the atmosphere, to measure it, to trace its past, and to model its future. Global warming skeptics often fall back on the argument that the scientific case for global warming is all model predictions, nothing but simulation; they warn us that we need to wait for real data, “sound science.” In A Vast Machine Paul Edwards has news for these skeptics: without models, there are no data. Today, no collection of signals or observations—even from satellites, which can “see” the whole planet with a single instrument—becomes global in time and space without passing through a series of data models. Everything we know about the world's climate we know through models. Edwards offers an engaging and innovative history of how scientists learned to understand the atmosphere—to measure it, trace its past, and model its future.
Author: Mariel Borowitz
Publisher: MIT Press
ISBN: 0262037181
Category : Technology & Engineering
Languages : en
Pages : 428
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Book Description
An examination of environmental satellite data sharing policies, offering a model of data-sharing policy development, case and practical recommendations for increasing global data sharing. Key to understanding and addressing climate change is continuous and precise monitoring of environmental conditions. Satellites play an important role in collecting climate data, offering comprehensive global coverage that can't be matched by in situ observation. And yet, as Mariel Borowitz shows in this book, much satellite data is not freely available but restricted; this remains true despite the data-sharing advocacy of international organizations and a global open data movement. Borowitz examines policies governing the sharing of environmental satellite data, offering a model of data-sharing policy development and applying it in case studies from the United States, Europe, and Japan—countries responsible for nearly half of the unclassified government Earth observation satellites. Borowitz develops a model that centers on the government agency as the primary actor while taking into account the roles of such outside actors as other government officials and non-governmental actors, as well as the economic, security, and normative attributes of the data itself. The case studies include the U.S. National Aeronautics and Space Administration (NASA) and the U.S. National Oceanographic and Atmospheric Association (NOAA), and the United States Geological Survey (USGS); the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT); and the Japanese Aerospace Exploration Agency (JAXA) and the Japanese Meteorological Agency (JMA). Finally, she considers the policy implications of her findings for the future and provides recommendations on how to increase global sharing of satellite data.
Author: Sian E. Harding
Publisher: MIT Press
ISBN: 0262548410
Category : Medical
Languages : en
Pages : 234
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Book Description
How science is opening up the mysteries of the heart, revealing the poetry in motion within the machine. Your heart is a miracle in motion, a marvel of construction unsurpassed by any human-made creation. It beats 100,000 times every day—if you were to live to 100, that would be more than 3 billion beats across your lifespan. Despite decades of effort in labs all over the world, we have not yet been able to replicate the heart’s perfect engineering. But, as Sian Harding shows us in The Exquisite Machine, new scientific developments are opening up the mysteries of the heart. And this explosion of new science—ultrafast imaging, gene editing, stem cells, artificial intelligence, and advanced sub-light microscopy—has crucial, real-world consequences for health and well-being. Harding—a world leader in cardiac research—explores the relation between the emotions and heart function, reporting that the heart not only responds to our emotions, it creates them as well. The condition known as Broken Heart Syndrome, for example, is a real disorder than can follow bereavement or stress. The Exquisite Machine describes the evolutionary forces that have shaped the heart’s response to damage, the astonishing rejuvenating power of stem cells, how we can avoid heart disease, and why it can be so hard to repair a damaged heart. It tells the stories of patients who have had the devastating experiences of a heart attack, chaotic heart rhythms, or stress-induced acute heart failure. And it describes how cutting-edge technologies are enabling experiments and clinical trials that will lead us to new solutions to the worldwide scourge of heart disease.
Author: Adrian Mackenzie
Publisher: MIT Press
ISBN: 0262036827
Category : Social Science
Languages : en
Pages : 269
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Book Description
If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought? Machine learning—programming computers to learn from data—has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking. Mackenzie focuses on machine learners—either humans and machines or human-machine relations—situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms—writing code and writing about code—and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures. Mackenzie's account of machine learning locates places in which a sense of agency can take root. His archaeology of the operational formation of machine learning does not unearth the footprint of a strategic monolith but reveals the local tributaries of force that feed into the generalization and plurality of the field.
Author: E.M. Forster
Publisher: Strelbytskyy Multimedia Publishing
ISBN:
Category : Literary Collections
Languages : en
Pages : 48
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Book Description
"The Machine Stops" by E.M. Forster, now presented in a beautifully illustrated edition, is a visionary and thought-provoking novella that explores the perils of technological dependency and the potential consequences of a society overly reliant on machines. Set in a future where humanity lives underground, isolated in individual cells, their every need attended to by an all-encompassing Machine, the story follows Vashti, a lecturer and true believer in the Machine's omnipotence. However, as the Machine begins to show signs of malfunction, Vashti's worldview is challenged, leading to a series of events that question the very foundations of her society. "The Machine Stops" remains a compelling exploration of the dangers of sacrificing human connections for the convenience of technology. This illustrated edition provides a fresh perspective on Forster's timeless work, making it an engaging and visually captivating experience for both new and returning readers.
Author: James Rodger Fleming
Publisher: MIT Press
ISBN: 0262033941
Category : Science
Languages : en
Pages : 307
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Book Description
"This big picture history of atmospheric research examines the first six decades of the twentieth century, from the dawn of applied fluid dynamics to the emergence, by 1960, of the interdisciplinary atmospheric sciences. Using newly available archival sources, it documents the work of three interconnected generations of scientists: Vilhelm Bjerknes, Carl-Gustaf Rossby, and Harry Wexler, whose aspirations were fueled by new theoretical insights, pressing societal needs, and expanded technological capabilities. Radio, radar, aviation, nuclear tracers, digital computing, sounding rockets, and satellites provided new ways to measure and study the global atmosphere -- a huge and dauntingly complex system. Bjerknes brought us a fundamental circulation theorem and founded the Bergen school of weather forecasting; Rossby established the graduate schools of meteorology at M.I.T., Chicago, and Stockholm, which focused on upper-air dynamics and, after 1947, on atmospheric environmental issues; and Wexler brought all the new technologies into the U.S. Weather Bureau and, with his colleague Jule Charney, prepared the foundations for the emergence of the interdisciplinary atmospheric sciences. This history weaves together cold war studies, military history, the rise of government research and development, and aviation and aeronautics with a nascent global awareness. It is a fascinating history of something we all experience--the weather --told through compelling historical characters"--Provided by publisher.
Author: Meredith Broussard
Publisher: MIT Press
ISBN: 026253701X
Category : Computers
Languages : en
Pages : 247
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Book Description
A guide to understanding the inner workings and outer limits of technology and why we should never assume that computers always get it right. In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally—hiring, driving, paying bills, even choosing romantic partners—that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology. With this book, she offers a guide to understanding the inner workings and outer limits of technology—and issues a warning that we should never assume that computers always get things right. Making a case against technochauvinism—the belief that technology is always the solution—Broussard argues that it's just not true that social problems would inevitably retreat before a digitally enabled Utopia. To prove her point, she undertakes a series of adventures in computer programming. She goes for an alarming ride in a driverless car, concluding “the cyborg future is not coming any time soon”; uses artificial intelligence to investigate why students can't pass standardized tests; deploys machine learning to predict which passengers survived the Titanic disaster; and attempts to repair the U.S. campaign finance system by building AI software. If we understand the limits of what we can do with technology, Broussard tells us, we can make better choices about what we should do with it to make the world better for everyone.
Author: Erik Brynjolfsson
Publisher: Brynjolfsson and McAfee
ISBN: 0984725113
Category : Business & Economics
Languages : en
Pages : 92
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Book Description
Examines how information technologies are affecting jobs, skills, wages, and the economy.
Author: Janet Vertesi
Publisher: Princeton University Press
ISBN: 0691187088
Category : Science
Languages : en
Pages : 568
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Book Description
Scholars across the humanities, social sciences, and information sciences are grappling with how best to study virtual environments, use computational tools in their research, and engage audiences with their results. Classic work in science and technology studies (STS) has played a central role in how these fields analyze digital technologies, but many of its key examples do not speak to today’s computational realities. This groundbreaking collection brings together a world-class group of contributors to refresh the canon for contemporary digital scholarship. In twenty-five pioneering and incisive essays, this unique digital field guide offers innovative new approaches to digital scholarship, the design of digital tools and objects, and the deployment of critically grounded technologies for analysis and discovery. Contributors cover a broad range of topics, including software development, hackathons, digitized objects, diversity in the tech sector, and distributed scientific collaborations. They discuss methodological considerations of social networks and data analysis, design projects that can translate STS concepts into durable scientific work, and much more. Featuring a concise introduction by Janet Vertesi and David Ribes and accompanied by an interactive microsite, this book provides new perspectives on digital scholarship that will shape the agenda for tomorrow’s generation of STS researchers and practitioners.
Author: Ian Goodfellow
Publisher: MIT Press
ISBN: 0262337371
Category : Computers
Languages : en
Pages : 801
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Book Description
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
